Identification and Stochastic Adaptive Control

1. Front Matter

   Pages i-xi

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2. Probability Theory Preliminaries

   • Chen Han-Fu, Lei Guo

   Pages 1-23

3. Limit Theorems on Martingales

   • Chen Han-Fu, Lei Guo

   Pages 25-50

4. Filtering and Control for Linear Systems

   • Chen Han-Fu, Lei Guo

   Pages 51-87

5. Coefficient Estimation for ARMAX Models

   • Chen Han-Fu, Lei Guo

   Pages 89-151

6. Stochastic Adaptive Tracking

   • Chen Han-Fu, Lei Guo

   Pages 153-186

7. Coefficient Estimation in Adaptive Control Systems

   • Chen Han-Fu, Lei Guo

   Pages 187-215

8. Order Estimation

   • Chen Han-Fu, Lei Guo

   Pages 217-241

9. Optimal Adaptive Control with Consistent Parameter Estimate

   • Chen Han-Fu, Lei Guo

   Pages 243-291

10. ARX(∞) Model Approximation

    • Chen Han-Fu, Lei Guo

    Pages 293-331

11. Estimation for Time-Varying Parameters

    • Chen Han-Fu, Lei Guo

    Pages 333-373

12. Adaptive Control of Time-Varying Stochastic Systems

    • Chen Han-Fu, Lei Guo

    Pages 375-401

13. Continuous-Time Stochastic Systems

    • Chen Han-Fu, Lei Guo

    Pages 403-419

14. Back Matter

    Pages 421-435

    PDF

Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo­ metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.

• Martingal
• Martingale
• Martingale difference sequence
• Parameter
• approximation
• calculus
• control
• control system
• evolution
• probability theory
• science
• stochastic differential equation
• stochastic systems

• Institute of Systems Science, Academia Sinica, Peking University, Beijing, China

  Han-Fu Chen, Lei Guo

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